Search Results (2,627)

With the globalized deployment of cross-border vehicle location services and the trajectory data, which contain user identity information and geographically sensitive features, the variability in privacy regulations in different jurisdictions can further exacerbate the technical and compliance challenges of data privacy protection. Traditional static differential privacy mechanisms struggle to accommodate spatiotemporal heterogeneity in dynamic scenarios because of the use of a fixed privacy budget parameter, leading to wasted privacy budgets or insufficient protection of sensitive regions. This study proposes a reinforcement-learning-based dynamic noise optimization method (DNO-RL) that dynamically adjusts the Laplacian noise scale by real-time sensing of vehicle density, region sensitivity, and the remaining privacy budget via a deep Q-network (DQN), with the aim of providing context-adaptive differential privacy protection for cross-border vehicle location services. Simulation experiments of cross-border scenarios based on the T-Drive dataset showed that DNO-RL reduced the average localization error by 28.3% and saved 17.9% of the privacy budget compared with the local differential privacy under the same privacy budget. This study provides a new paradigm for the dynamic privacy–utility balancing of cross-border vehicular networking services. Full article

GPSs typically have an accuracy ranging from a few meters to several tens of meters. However, when corrected using various methods, they can achieve an accuracy of several tens of centimeters. In autonomous driving, a positioning accuracy of less than 50 cm is required for lane-level positioning, route generation, and navigation. However, in environments where GPS signals are blocked, such as tunnels and underground roads, absolute positioning is impossible. Instead, relative positioning methods integrating IMU, IVN, and cameras are used. These methods are influenced by numerous variables, however, such as vehicle speed and road conditions, resulting in lower accuracy. In this study, we conducted experiments on current vehicle navigation technologies using an autonomous driving simulation vehicle in the Suri–Suam Tunnel of the Seoul Metropolitan Area 1st Ring Expressway. To recognize objects (lane markings/2D/3D) for position correction inside the tunnel, data on tunnel and underground road infrastructure in Seoul and Gyeonggi Province was collected, processed, refined, and trained. Additionally, a Loosely Coupled-based Kalman Filter was designed and applied for the fusion of GPSs, IMUs, and IVNs. As a result, an error of 113.62 cm was observed in certain sections. This suggests that while the technology is applicable for general vehicle lane-level navigation in ultra-long tunnels spanning several kilometers for public service, it falls short of meeting the precision required for autonomous driving systems, which demand lane-level accuracy. Therefore, it was concluded that infrastructure-based absolute positioning technology is necessary to enable precise navigation inside tunnels. Full article

With the development of wave energy, a promising renewable resource, oscillating water column (OWC) devices, has been extensively studied for its potential in harnessing this energy. However, traditional OWC devices face challenges such as corrosion and damage from prolonged exposure to harsh marine environments, limiting their long-term viability and efficiency. To address these limitations, this paper proposes a novel U-tube type dual chamber OWC wave energy conversion device integrated within a marine vehicle. The research involves the design of a U-tube dual-chamber OWC device, which utilizes the pitch motion of a marine vehicle to drive the oscillation of water columns within the U-tube, generating reciprocating airflow that drives an air turbine. Numerical simulations using computational fluid dynamics (CFD) were conducted to analyze the effects of various structural dimensions, including device length, width, air chamber height, U-tube channel width, and bottom channel height, on the aerodynamic power output. The simulations considered real sea conditions, focusing on low-frequency waves prevalent in China’s sea areas. Simulation results reveal that increasing the device’s length and width substantially boosts aerodynamic power, while air chamber height and U-tube channel width have minor effects. These findings provide valuable insights into the optimal design of U-tube dual-chamber OWC devices for efficient wave energy conversion, laying the foundation for future physical prototype development and experimental validation. Full article

Emerging transportation challenges necessitate cutting-edge technologies for real-time infrastructure and traffic monitoring. To create a dynamic digital twin for intersection monitoring, data gathering, performance assessment, and calibration of microsimulation software, this study presents a state-of-the-art platform that combines high-resolution LiDAR sensor data with VISSIM simulation software. Intending to track traffic flow and evaluate important factors, including congestion, delays, and lane configurations, the platform gathers and analyzes real-time data. The technology allows proactive actions to improve safety and reduce interruptions by utilizing the comprehensive information that LiDAR provides, such as vehicle trajectories, speed profiles, and lane changes. The digital twin technique offers unparalleled precision in traffic and infrastructure state monitoring by fusing real data streams with simulation-based performance analysis. The results show how the platform can transform real-time monitoring and open the door to data-driven decision-making, safer intersections, and more intelligent traffic data collection methods. Using the proposed platform, this study calibrated a VISSIM simulation network to optimize the driving behavior parameters in the software. This study addresses current issues in urban traffic management with real-time solutions, demonstrating the revolutionary impact of emerging technology in intelligent infrastructure monitoring. Full article

With the popularization of electric vehicles and the continuous expansion of the electric vehicle market, the construction and management of charging facilities for electric vehicles have become important issues in research and practice. In some remote areas, the charging stations are idle due to low traffic flow, resulting in a waste of resources. Areas with high traffic flow may have fewer charging stations, resulting in long queues and road congestion. The purpose of this study is to optimize the location of charging stations and the number of charging piles in the stations based on the distribution of traffic flow, and to construct a bi-level programming model by analyzing the distribution of traffic flow. The upper-level planning model is the user-balanced flow allocation model, which is solved to obtain the optimal traffic flow allocation of the road network, and the output of the upper-level planning model is used as the input of the lower-layer model. The lower-level planning model is a generalized minimum cost model with driving time, charging waiting time, charging time, and the cost of electricity consumed to reach the destination of the trip as objective functions. In this study, an empirical simulation is conducted on the road network of Hefei City, Anhui Province, utilizing three algorithms—GA, GWO, and PSO—for optimization and sensitivity analysis. The optimized results are compared with the existing charging station deployment scheme in the road network to demonstrate the effectiveness of the proposed methodology. Full article

This paper presents an advanced energy management system (EMS) for optimizing power distribution in a battery/ultracapacitor (UC) hybrid energy storage system (HESS) for electric vehicles (EVs). The proposed EMS accounts for all energy flow scenarios within a practical driving cycle. A regenerative braking control strategy is developed to maximize kinetic energy recovery using an induction motor, efficiently distributing the recovered energy between the UC and battery. Additionally, a power flow management approach is introduced for both motoring (discharge) and braking (charge) operations via bidirectional buck–boost DC-DC converters. In discharge mode, an optimal distribution factor is dynamically adjusted to balance power delivery between the battery and UC, maximizing efficiency. During charging, a DC link voltage control mechanism prioritizes UC charging over the battery, reducing stress and enhancing energy recovery efficiency. The proposed EMS is validated through simulations and experiments, demonstrating significant improvements in vehicle acceleration, energy efficiency, and battery lifespan. Full article

This paper introduces a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm designed to enhance autonomous vehicle navigation on highways. The LSPP approach prioritizes smooth maneuvering, obstacle avoidance, passenger comfort, and adherence to road constraints, including lane boundaries, through optimized trajectory generation using quintic spline functions and a dynamic speed profile. Leveraging real-time data from the vehicle’s sensor fusion module, the LSPP algorithm accurately interprets the positions of surrounding vehicles and obstacles, creating a safe, dynamically feasible path that is relayed to the Model Predictive Control (MPC) track-following module for precise execution. The theoretical distinction of LSPP lies in its modular integration of: (1) a finite state machine (FSM)-based decision-making layer that selects maneuver-specific goal states (e.g., keep lane, change lane left/right); (2) quintic spline optimization to generate smooth, jerk-minimized, and kinematically consistent trajectories; (3) a multi-objective cost evaluation framework that ranks competing paths according to safety, comfort, and efficiency; and (4) a closed-loop MPC controller to ensure real-time trajectory execution with robustness. Extensive simulations conducted in diverse highway scenarios and traffic conditions demonstrate LSPP’s effectiveness in delivering smooth, safe, and computationally efficient trajectories. Results show consistent improvements in lane-keeping accuracy, collision avoidance, enhanced materials wear performance, and planning responsiveness compared to traditional path-planning methods. These findings confirm LSPP’s potential as a practical and high-performance solution for autonomous highway driving. Full article

With the electrification of generator sets, electric locomotives, new energy vehicles, and other industries, AC motors subject bearings to an electric field environment, leading to galvanic corrosion due to the use of variable frequency power supply drives. The phenomenon of bearing discharge breakdown in subway traction motors is a critical issue in understanding the relationship between shaft current strength and the extent of bearing damage. This paper analyzes the mechanism of impulse discharge that leads to galvanic corrosion damage in bearings at a microscopic level and conducts electric thermal coupling simulations of the traction motor bearing discharge breakdown process. It examines the temperature rise associated with lubricant film discharge breakdown during the dynamic operation of the bearing and investigates how breakdown channel parameters and operational conditions affect the temperature rise in the micro-region of bearing lubrication. Ultimately, the results of the electric thermal coupling simulation are validated through experimental tests. This study revealed that in an electric field environment, the load-bearing area of the outer ring experiences significantly more severe corrosion damage than the inner ring, whereas non-bearing areas remain unaffected by electrolytic corrosion. When the inner ring reaches a speed of 4500_rpm, the maximum widths of electrolytic corrosion pits for the outer and inner rings are measured at 89 um and 51 um, respectively. Additionally, the highest recorded temperatures for the breakdown channels in the outer and inner rings are 932 °C and 802 °C, respectively. Furthermore, as the inner ring speed increases, both the width of the electrolytic corrosion pits and the temperature of the breakdown channels rise. Specifically, at inner ring speeds of 2500_rpm, 3500_rpm, and 4500_rpm, the widths of the electrolytic pits in the outer ring raceway load zone were measured at 34 um, 56 um, and 89 um, respectively. The highest temperatures of the lubrication film breakdown channels were recorded as 612 °C, 788 °C, and 932 °C, respectively. This study provides a theoretical basis and data support for the protective and maintenance practices of traction motor bearings. Full article

Inspired by the Toyota Mirai, this study presents a high-fidelity data-driven approach for modelling and simulation of a fuel cell hybrid electric powertrain. This study utilises technical assessment data sourced from Argonne National Laboratory’s publicly available report, faithfully modelling most of the vehicle subsystems as data-driven entities. The simulation framework is developed in the MATLAB/Simulink environment and is based on a power dynamics approach, capturing nonlinear interactions and performance intricacies between different powertrain elements. This study investigates subsystem synergies and performance boundaries under a combined driving cycle composed of the NEDC, WLTP Class 3 and US06 profiles, representing urban, extra-urban and aggressive highway conditions. To emulate the real-world load-following strategy, a state transition power management and allocation method is synthesised. The proposed method dynamically governs the power flow between the fuel cell stack and the traction battery across three operational states, allowing the battery to stay within its allocated bounds. This simulation framework offers a near-accurate and computationally efficient digital counterpart to a commercial hybrid powertrain, serving as a valuable tool for educational and research purposes. Full article

This work proposes a new method to refill fuel cell electric vehicle hydrogen tanks from a storage system in hydrogen refueling stations. The new method uses the storage tanks in cascade to supply hydrogen to the refueling station dispensers. This method reduces the hydrogen compressor power requirement and the energy consumption for refilling the vehicle tank; therefore, the proposed alternative design for hydrogen refueling stations is feasible and compatible with low-intensity renewable energy sources like solar photovoltaic, wind farms, or micro-hydro plants. Additionally, the cascade method supplies higher pressure to the dispenser throughout the day, thus reducing the refueling time for specific vehicle driving ranges. The simulation shows that the energy saving using the cascade method achieves 9% to 45%, depending on the vehicle attendance. The hydrogen refueling station design supports a daily vehicle attendance of 9 to 36 with a complete refueling process coverage. The carried-out simulation proves that the vehicle tank achieves the maximum attainable pressure of 700 bars with a storage system of six tanks. The data analysis shows that the daily hourly hydrogen demand follows a sinusoidal function, providing a practical tool to predict the hydrogen demand for any vehicle attendance, allowing the planners and station designers to resize the elements to fulfill the new requirements. The proposed system is also applicable to hydrogen ICE vehicles. Full article

To improve the stability and safety of electric vehicles during medium-to-high-speed cornering, this paper investigates torque differential control for dual rear-wheel hub motor drive systems, extending beyond traditional speed control based on the Ackermann steering model. A nonlinear three-degree-of-freedom vehicle dynamics model incorporating the Dugoff tire model was established. By introducing the maximum correntropy criterion, an unscented Kalman filter was developed to estimate longitudinal velocity, sideslip angle at the center of mass, and yaw rate. Building upon the speed differential control achieved through Ackermann steering model-based rear-wheel speed calculation, improvements were made to the conventional exponential reaching law, while a novel switching function was proposed to formulate a new sliding mode controller for computing an additional yaw moment to realize torque differential control. Finally, simulations conducted on the Carsim/Simulink platform demonstrated that the maximum correntropy criterion unscented Kalman filter effectively improves estimation accuracy, achieving at least a 22.00% reduction in RMSE metrics compared to conventional unscented Kalman filter. With torque control exhibiting higher vehicle stability than speed control, the RMSE values of yaw rate and sideslip angle at the center of mass are reduced by at least 20.00% and 4.55%, respectively, enabling stable operation during medium-to-high-speed cornering conditions. Full article

Aiming at the multifunctional requirements of the limited space in high-end vehicle side-door latches, a double single-loop RRUPRR mechanism driven by a single motor for both electric releasing and cinching is proposed based on the POC set. The kinematical equations of the RRURR mechanism possess 2 × 2 analytical solutions. In order to apply the current motion/force transmission performance index of the parallel mechanisms to the transmission performance analysis of the serial mechanisms, matching methods for chain-driving transference and the moving/fixed platform inversion are proposed. The solution of the performance index of a single-degree-of-freedom single-loop mechanism is equivalent to the solution of the input motion/force transmission performance index of a parallel mechanism. The overall motion/force transmission performance index of a single-loop mechanism is constructed, and the corresponding calculation procedure is defined. Chain-driving transference can be obtained through forward and inverse solutions of the RRURR mechanism. In response to the extremely high requirements for motion/force transmission performance of electric release mechanisms, the proposed overall motion/force transmission performance index is used to calculate for the input motion screw and corresponding transmission-force screw of the single-loop RRURR mechanism and obtain the overall motion/force transmission performance of the mechanism. The performance atlas of the mechanism shows that it has excellent motion/force transmission characteristics within the workspace. Using ADAMS simulation software, the driving torque required for electric releasing and cinching of a vehicle side-door latch mechanism with a single motor is analyzed. The overall motion/force transmission performance index of a single-loop mechanism can be applied to single-loop overconstrained mechanisms and non-overconstrained mechanisms. Full article

Autonomous driving systems have the potential to reduce traffic accidents dramatically; however, conventional modules often struggle to accurately detect risks in complex environments. This study presents a novel risk recognition system that integrates the reasoning capabilities of a large language model (LLM), specifically GPT-4, with traffic engineering domain knowledge. By incorporating surrogate safety measures such as time-to-collision (TTC) alongside traditional sensor and image data, our approach enhances the vehicle’s ability to interpret and react to potentially dangerous situations. Utilizing the realistic 3D simulation environment of CARLA, the proposed framework extracts comprehensive data—including object identification, distance, TTC, and vehicle dynamics—and reformulates this information into natural language inputs for GPT-4. The LLM then provides risk assessments with detailed justifications, guiding the autonomous vehicle to execute appropriate control commands. The experimental results demonstrate that the LLM-based module outperforms conventional systems by maintaining safer distances, achieving more stable TTC values, and delivering smoother acceleration control during dangerous scenarios. This fusion of LLM reasoning with traffic engineering principles not only improves the reliability of risk recognition but also lays a robust foundation for future real-time applications and dataset development in autonomous driving safety. Full article

The automotive suspension must perform competently to support comfort and safety when driving. Traditionally, car suspension control tuning is performed through trial and error or with classical techniques that cannot guarantee optimal performance under varying road conditions. The study aims at designing a Linear Quadratic Regulator-based Bacterial Memetic Algorithm (LQR-BMA) for suspension systems of automobiles. BMA combines the bacterial foraging optimization algorithm (BFOA) and the memetic algorithm (MA) to enhance the effectiveness of its search process. An LQR control system adjusts the suspension’s behavior by determining the optimal feedback gains using BMA. The control objective is to significantly reduce the random vibration and oscillation of both the vehicle and the suspension system while driving, thereby making the ride smoother and enhancing road handling. The BMA adopts control parameters that support biological attraction, reproduction, and elimination-dispersal processes to accelerate the search and enhance the program’s stability. By using an algorithm, it explores several parts of space and improves its value to determine the optimal setting for the control gains. MATLAB 2024b software is used to run simulations with a randomly generated road profile that has a power spectral density (PSD) value obtained using the Fast Fourier Transform (FFT) method. The results of the LQR-BMA are compared with those of the optimized LQR based on the genetic algorithm (LQR-GA) and the Virus Evolutionary Genetic Algorithm (LQR-VEGA) to substantiate the potency of the proposed model. The outcomes reveal that the LQR-BMA effectuates efficient and highly stable control system performance compared to the LQR-GA and LQR-VEGA methods. From the results, the BMA-optimized model achieves reductions of 77.78%, 60.96%, 70.37%, and 73.81% in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the GA-optimized model. Moreover, the BMA-optimized model achieved a −59.57%, 38.76%, 94.67%, and 95.49% reduction in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the VEGA-optimized model. Full article

The design and optimization of drive distribution strategies are critical for enhancing the performance of Formula Student electric racing cars, which face demanding operational conditions such as rapid acceleration, tight cornering, and variable track surfaces. Given the increasing complexity of racing environments and the need for adaptive control solutions, a multi-mode adaptive drive distribution strategy for four-wheel-drive Formula Student electric racing cars is proposed in this study to meet specialized operational demands. Based on the dynamic characteristics of standardized test scenarios (e.g., straight-line acceleration and figure-eight loop), two control modes are designed: slip-ratio-based anti-slip control for longitudinal dynamics and direct yaw moment control for lateral stability. A CarSim–Simulink co-simulation platform is established, with test scenarios conforming to competition standards, including variable road adhesion coefficients (μ is 0.3–0.9) and composite curves. Simulation results indicate that, compared to conventional PID control, the proposed strategy reduces the peak slip ratio to the optimal range of 18% during acceleration and enhances lateral stability in the figure-eight loop, maintaining the sideslip angle around −0.3°. These findings demonstrate the potential for significant improvements in both performance and safety, offering a scalable framework for future developments in racing vehicle control systems. Full article